improved organization and qualification of existing technical information, thereby enabling projects previously affected by documentation inconsistencies to produce more coherent and reproducible technical outputs. Indicative metrics derived from Mining Assessment deployments include: (i) Early-stage nonconformity detection: approximately 70% of total non-conformities historically identified at financing/regulatory submission that were detected at early or mid-project stages after implementation, shifting the correction effort to lower-cost stages; (ii) Audit and due diligence preparation time: a reduction of approximately approximate 25% reduction in preparation time was reported by participating teams once assessment routines were embedded in project management; (iii) Reporting rework: latestage technical revisions linked to documentation inconsistencies decreased by an estimated approximate 50% reduction in revision cycles, with corresponding effects on submission turnaround; (iv) Coverage of compliance items: the share of CRIRSCO-aligned reporting items with documented evidence at first internal review increased from a baseline of 50% to 75% in post-implementation across pilot deployments. As with Case Study 1, these indicators are project-specific and should be calibrated to local reporting frameworks and organizational baselines; they are presented as evidence of direction and order of magnitude rather than as universal benchmarks. 5. DISCUSSION Results from GDQM and Mining Assessment implementations indicate that while accelerating mineral project development entails compressing technical stages, it is highly dependent on reducing uncertainty through improved data governance, technical assurance, and decision traceability. However, broader adoption of such frameworks faces practical challenges that extend beyond technical implementation. Database qualification, therefore, becomes not only a technical improvement but a strategic lever to accelerate responsible mineral project development while preserving transparency and technical reliability. Applications demonstrate that the approach is relevant across different project contexts, including greenfield exploration, brownfield operations requiring database consolidation, and projects under technical or investment review. However, successful transferability requires adaptation to jurisdictional and organizational conditions, as regulatory, social, and institutional contexts vary between regions. Despite positive operational outcomes, important limitations remain. AI-assisted validation does not replace professional judgment, and expert accountability remains essential for mineral project development. To make this boundary explicit, AI-assisted routines deployed within TIME TO MINE operate primarily at the data qualification and screening layer. They are used to identify inconsistencies in drillhole and assay databases (e.g., depth mismatches between relational tables, duplicated records, nonstandardized geological coding), to flag outliers and gaps in QA/QC records (blanks, standards, duplicates), to check completeness of documentation against reporting checklists, and to support spatial diagnostics of data-quality variability across a deposit. They do not perform geological interpretation, do not assign Mineral Resource or Reserve categories, and do not replace the judgment of the Competent or Qualified Person, whose explicit accountability for classification, modifying-factor assessment, and
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